COST10-BP01 - Develop a workload review process

import boto3
import json
import pandas as pd
from datetime import datetime, timedelta
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple
from enum import Enum
import requests
import logging

class ReviewType(Enum):
    QUARTERLY = "quarterly"
    ANNUAL = "annual"
    TRIGGERED = "triggered"
    CONTINUOUS = "continuous"

class OptimizationCategory(Enum):
    COST_REDUCTION = "cost_reduction"
    PERFORMANCE_IMPROVEMENT = "performance_improvement"
    OPERATIONAL_EFFICIENCY = "operational_efficiency"
    SECURITY_ENHANCEMENT = "security_enhancement"
    COMPLIANCE_IMPROVEMENT = "compliance_improvement"

@dataclass
class WorkloadProfile:
    workload_id: str
    workload_name: str
    business_criticality: str  # critical, important, standard
    current_monthly_cost: float
    architecture_type: str
    last_review_date: datetime
    next_review_date: datetime
    optimization_opportunities: List[str]

@dataclass
class ServiceEvaluation:
    service_name: str
    evaluation_date: datetime
    applicability_score: float  # 0-1
    cost_impact: float  # positive = savings, negative = increase
    implementation_effort: str  # low, medium, high
    risk_level: str  # low, medium, high
    recommendation: str  # adopt, pilot, defer, reject
    rationale: str

@dataclass
class ReviewOutcome:
    review_id: str
    workload_id: str
    review_date: datetime
    review_type: ReviewType
    findings: List[str]
    recommendations: List[Dict]
    estimated_savings: float
    implementation_priority: str
    next_review_date: datetime

class WorkloadReviewManager:
    def __init__(self):
        self.wellarchitected = boto3.client('wellarchitected')
        self.config = boto3.client('config')
        self.ce_client = boto3.client('ce')
        self.trusted_advisor = boto3.client('support')
        self.systems_manager = boto3.client('ssm')
        
        # Review configuration
        self.review_config = {
            'quarterly_review_scope': ['cost_optimization', 'new_services'],
            'annual_review_scope': ['full_architecture', 'strategic_alignment'],
            'triggered_review_triggers': ['cost_spike', 'performance_degradation', 'new_service_announcement'],
            'continuous_monitoring_metrics': ['cost_trends', 'utilization', 'performance']
        }
        
        # Setup logging
        logging.basicConfig(level=logging.INFO)
        self.logger = logging.getLogger(__name__)
    
    def create_workload_review_process(self, process_config: Dict) -> Dict:
        """Create comprehensive workload review process"""
        
        review_process = {
            'process_id': f"WRP_{datetime.now().strftime('%Y%m%d')}",
            'process_name': process_config['name'],
            'review_framework': self.create_review_framework(process_config),
            'evaluation_criteria': self.define_evaluation_criteria(process_config),
            'review_schedules': self.create_review_schedules(process_config),
            'governance_structure': self.define_governance_structure(process_config),
            'automation_components': self.create_automation_components(process_config),
            'reporting_framework': self.create_reporting_framework(process_config)
        }
        
        return review_process
    
    def create_review_framework(self, config: Dict) -> Dict:
        """Create structured review framework"""
        
        framework = {
            'review_phases': {
                'discovery': {
                    'duration': '1 week',
                    'activities': [
                        'Monitor new AWS service announcements',
                        'Collect workload performance and cost data',
                        'Identify potential optimization opportunities',
                        'Gather stakeholder input and requirements'
                    ],
                    'deliverables': ['Service announcement summary', 'Workload baseline report']
                },
                'assessment': {
                    'duration': '2 weeks',
                    'activities': [
                        'Evaluate new services against workload requirements',
                        'Perform cost-benefit analysis',
                        'Assess technical feasibility and risks',
                        'Compare alternatives and options'
                    ],
                    'deliverables': ['Service evaluation report', 'Cost-benefit analysis', 'Risk assessment']
                },
                'planning': {
                    'duration': '1 week',
                    'activities': [
                        'Prioritize optimization opportunities',
                        'Create implementation roadmap',
                        'Define success metrics and KPIs',
                        'Prepare business case and recommendations'
                    ],
                    'deliverables': ['Implementation plan', 'Business case', 'Success metrics']
                },
                'decision': {
                    'duration': '1 week',
                    'activities': [
                        'Present findings to stakeholders',
                        'Make go/no-go decisions',
                        'Approve implementation plans',
                        'Allocate resources and timeline'
                    ],
                    'deliverables': ['Decision record', 'Approved implementation plan']
                }
            },
            'review_types': {
                'comprehensive': {
                    'frequency': 'annual',
                    'scope': 'full_workload_architecture',
                    'duration': '4-6 weeks',
                    'stakeholders': ['technical_teams', 'business_owners', 'finance', 'security']
                },
                'focused': {
                    'frequency': 'quarterly',
                    'scope': 'specific_optimization_areas',
                    'duration': '2-3 weeks',
                    'stakeholders': ['technical_teams', 'cost_optimization_team']
                },
                'rapid': {
                    'frequency': 'monthly',
                    'scope': 'new_service_evaluation',
                    'duration': '1 week',
                    'stakeholders': ['technical_leads', 'architects']
                }
            }
        }
        
        return framework
    
    def define_evaluation_criteria(self, config: Dict) -> Dict:
        """Define comprehensive evaluation criteria for new services"""
        
        criteria = {
            'cost_impact': {
                'weight': 0.3,
                'metrics': [
                    'Direct cost comparison (current vs new service)',
                    'Migration and implementation costs',
                    'Operational cost changes',
                    'Total cost of ownership over 3 years'
                ],
                'scoring': {
                    'excellent': '>30% cost reduction',
                    'good': '15-30% cost reduction',
                    'neutral': '±15% cost impact',
                    'poor': '>15% cost increase'
                }
            },
            'technical_fit': {
                'weight': 0.25,
                'metrics': [
                    'Functional requirements alignment',
                    'Performance characteristics match',
                    'Integration complexity',
                    'Scalability and reliability'
                ],
                'scoring': {
                    'excellent': 'Perfect fit with enhanced capabilities',
                    'good': 'Good fit with minor gaps',
                    'neutral': 'Adequate fit with workarounds',
                    'poor': 'Poor fit requiring significant changes'
                }
            },
            'implementation_effort': {
                'weight': 0.2,
                'metrics': [
                    'Migration complexity and duration',
                    'Required skill development',
                    'Infrastructure changes needed',
                    'Testing and validation effort'
                ],
                'scoring': {
                    'excellent': 'Minimal effort, drop-in replacement',
                    'good': 'Moderate effort, straightforward migration',
                    'neutral': 'Significant effort, complex migration',
                    'poor': 'Extensive effort, major architectural changes'
                }
            },
            'risk_assessment': {
                'weight': 0.15,
                'metrics': [
                    'Technical risks and unknowns',
                    'Business continuity impact',
                    'Vendor lock-in considerations',
                    'Compliance and security implications'
                ],
                'scoring': {
                    'excellent': 'Very low risk, proven technology',
                    'good': 'Low risk, manageable concerns',
                    'neutral': 'Medium risk, requires mitigation',
                    'poor': 'High risk, significant concerns'
                }
            },
            'strategic_alignment': {
                'weight': 0.1,
                'metrics': [
                    'Alignment with business objectives',
                    'Support for future growth plans',
                    'Technology roadmap consistency',
                    'Competitive advantage potential'
                ],
                'scoring': {
                    'excellent': 'Strong strategic alignment',
                    'good': 'Good alignment with benefits',
                    'neutral': 'Neutral impact on strategy',
                    'poor': 'Misaligned with strategic direction'
                }
            }
        }
        
        return criteria
    
    def conduct_workload_review(self, workload_profile: WorkloadProfile, 
                              review_type: ReviewType) -> ReviewOutcome:
        """Conduct comprehensive workload review"""
        
        review_id = f"WR_{workload_profile.workload_id}_{datetime.now().strftime('%Y%m%d')}"
        
        # Collect workload data
        workload_data = self.collect_workload_data(workload_profile.workload_id)
        
        # Identify new services for evaluation
        new_services = self.identify_relevant_new_services(workload_data)
        
        # Evaluate each new service
        service_evaluations = []
        for service in new_services:
            evaluation = self.evaluate_service_for_workload(service, workload_data)
            service_evaluations.append(evaluation)
        
        # Generate findings and recommendations
        findings = self.generate_review_findings(workload_data, service_evaluations)
        recommendations = self.generate_recommendations(service_evaluations, workload_profile)
        
        # Calculate estimated savings
        estimated_savings = sum(
            eval.cost_impact for eval in service_evaluations 
            if eval.cost_impact > 0 and eval.recommendation in ['adopt', 'pilot']
        )
        
        # Determine next review date
        next_review_date = self.calculate_next_review_date(review_type, findings)
        
        review_outcome = ReviewOutcome(
            review_id=review_id,
            workload_id=workload_profile.workload_id,
            review_date=datetime.now(),
            review_type=review_type,
            findings=findings,
            recommendations=recommendations,
            estimated_savings=estimated_savings,
            implementation_priority=self.determine_implementation_priority(recommendations),
            next_review_date=next_review_date
        )
        
        return review_outcome
    
    def collect_workload_data(self, workload_id: str) -> Dict:
        """Collect comprehensive workload data for review"""
        
        workload_data = {
            'workload_id': workload_id,
            'architecture_components': self.get_architecture_components(workload_id),
            'cost_data': self.get_workload_costs(workload_id),
            'performance_metrics': self.get_performance_metrics(workload_id),
            'utilization_data': self.get_utilization_data(workload_id),
            'compliance_status': self.get_compliance_status(workload_id),
            'well_architected_review': self.get_well_architected_status(workload_id)
        }
        
        return workload_data
    
    def identify_relevant_new_services(self, workload_data: Dict) -> List[Dict]:
        """Identify new AWS services relevant to the workload"""
        
        # This would integrate with AWS What's New API or RSS feed
        # For demonstration, returning sample new services
        
        architecture_components = workload_data.get('architecture_components', [])
        relevant_services = []
        
        # Example logic for identifying relevant services
        if 'EC2' in architecture_components:
            relevant_services.extend([
                {
                    'service_name': 'AWS Graviton3 Instances',
                    'category': 'compute',
                    'announcement_date': '2024-01-15',
                    'relevance_score': 0.8,
                    'description': 'Next-generation ARM-based instances with better price-performance'
                },
                {
                    'service_name': 'Amazon EC2 M7i Instances',
                    'category': 'compute',
                    'announcement_date': '2024-01-10',
                    'relevance_score': 0.7,
                    'description': 'Latest generation general-purpose instances'
                }
            ])
        
        if 'RDS' in architecture_components:
            relevant_services.append({
                'service_name': 'Amazon Aurora Serverless v2',
                'category': 'database',
                'announcement_date': '2024-01-05',
                'relevance_score': 0.9,
                'description': 'Serverless database with instant scaling capabilities'
            })
        
        if 'Lambda' in architecture_components:
            relevant_services.append({
                'service_name': 'AWS Lambda SnapStart',
                'category': 'serverless',
                'announcement_date': '2024-01-12',
                'relevance_score': 0.6,
                'description': 'Reduce cold start times for Java Lambda functions'
            })
        
        return relevant_services
    
    def evaluate_service_for_workload(self, service: Dict, workload_data: Dict) -> ServiceEvaluation:
        """Evaluate a specific service for workload adoption"""
        
        # Calculate applicability score based on workload characteristics
        applicability_score = self.calculate_applicability_score(service, workload_data)
        
        # Estimate cost impact
        cost_impact = self.estimate_cost_impact(service, workload_data)
        
        # Assess implementation effort
        implementation_effort = self.assess_implementation_effort(service, workload_data)
        
        # Evaluate risks
        risk_level = self.evaluate_risks(service, workload_data)
        
        # Generate recommendation
        recommendation = self.generate_service_recommendation(
            applicability_score, cost_impact, implementation_effort, risk_level
        )
        
        # Create rationale
        rationale = self.create_evaluation_rationale(
            service, applicability_score, cost_impact, implementation_effort, risk_level
        )
        
        evaluation = ServiceEvaluation(
            service_name=service['service_name'],
            evaluation_date=datetime.now(),
            applicability_score=applicability_score,
            cost_impact=cost_impact,
            implementation_effort=implementation_effort,
            risk_level=risk_level,
            recommendation=recommendation,
            rationale=rationale
        )
        
        return evaluation
    
    def create_review_automation(self, automation_config: Dict) -> Dict:
        """Create automation components for workload reviews"""
        
        automation_framework = {
            'service_monitoring': {
                'aws_whats_new_monitor': self.create_service_announcement_monitor(),
                'pricing_change_monitor': self.create_pricing_change_monitor(),
                'feature_update_monitor': self.create_feature_update_monitor()
            },
            'data_collection': {
                'workload_inventory': self.create_workload_inventory_automation(),
                'cost_data_collection': self.create_cost_data_automation(),
                'performance_monitoring': self.create_performance_monitoring_automation()
            },
            'evaluation_automation': {
                'service_matching': self.create_service_matching_automation(),
                'cost_impact_calculator': self.create_cost_impact_calculator(),
                'risk_assessment_automation': self.create_risk_assessment_automation()
            },
            'reporting_automation': {
                'review_report_generation': self.create_report_generation_automation(),
                'dashboard_updates': self.create_dashboard_automation(),
                'notification_system': self.create_notification_automation()
            }
        }
        
        return automation_framework
    
    def create_service_announcement_monitor(self) -> Dict:
        """Create automation for monitoring AWS service announcements"""
        
        monitor_config = {
            'data_sources': [
                'AWS What\'s New RSS feed',
                'AWS Blog posts',
                'AWS re:Invent announcements',
                'AWS Summit presentations'
            ],
            'filtering_criteria': [
                'Cost optimization related',
                'Performance improvements',
                'New service launches',
                'Pricing model changes'
            ],
            'processing_pipeline': {
                'data_ingestion': 'Lambda function triggered by RSS updates',
                'content_analysis': 'Natural language processing to categorize announcements',
                'relevance_scoring': 'ML model to score relevance to existing workloads',
                'notification_routing': 'Send relevant announcements to appropriate teams'
            },
            'output_format': {
                'structured_data': 'JSON format with metadata',
                'summary_reports': 'Weekly digest of relevant announcements',
                'priority_alerts': 'Immediate notifications for high-impact announcements'
            }
        }
        
        return monitor_config
    
    def generate_review_dashboard(self, review_data: List[ReviewOutcome]) -> Dict:
        """Generate comprehensive review dashboard"""
        
        dashboard_config = {
            'dashboard_name': 'Workload Review Management',
            'widgets': [
                {
                    'type': 'metric',
                    'title': 'Review Completion Rate',
                    'metrics': [
                        ['Custom/WorkloadReview', 'ReviewsCompleted'],
                        ['Custom/WorkloadReview', 'ReviewsScheduled'],
                        ['Custom/WorkloadReview', 'ReviewsOverdue']
                    ],
                    'period': 86400
                },
                {
                    'type': 'metric',
                    'title': 'Optimization Opportunities Identified',
                    'metrics': [
                        ['Custom/WorkloadReview', 'OptimizationOpportunities'],
                        ['Custom/WorkloadReview', 'EstimatedSavings'],
                        ['Custom/WorkloadReview', 'ImplementedOptimizations']
                    ],
                    'period': 86400
                },
                {
                    'type': 'table',
                    'title': 'Recent Review Outcomes',
                    'columns': ['Workload', 'Review Date', 'Findings', 'Estimated Savings', 'Priority'],
                    'data_source': 'review_outcomes_table'
                },
                {
                    'type': 'pie_chart',
                    'title': 'Service Evaluation Recommendations',
                    'metrics': [
                        ['Custom/WorkloadReview', 'RecommendationAdopt'],
                        ['Custom/WorkloadReview', 'RecommendationPilot'],
                        ['Custom/WorkloadReview', 'RecommendationDefer'],
                        ['Custom/WorkloadReview', 'RecommendationReject']
                    ],
                    'period': 86400
                }
            ],
            'summary_metrics': self.calculate_review_summary_metrics(review_data)
        }
        
        return dashboard_config
    
    def calculate_review_summary_metrics(self, review_data: List[ReviewOutcome]) -> Dict:
        """Calculate summary metrics for review dashboard"""
        
        if not review_data:
            return {}
        
        total_reviews = len(review_data)
        total_estimated_savings = sum(review.estimated_savings for review in review_data)
        
        # Calculate recommendation distribution
        recommendations = []
        for review in review_data:
            for rec in review.recommendations:
                recommendations.append(rec.get('recommendation', 'unknown'))
        
        recommendation_counts = {}
        for rec in recommendations:
            recommendation_counts[rec] = recommendation_counts.get(rec, 0) + 1
        
        summary = {
            'total_reviews_completed': total_reviews,
            'total_estimated_savings': total_estimated_savings,
            'average_savings_per_review': total_estimated_savings / total_reviews if total_reviews > 0 else 0,
            'recommendation_distribution': recommendation_counts,
            'high_priority_reviews': len([r for r in review_data if r.implementation_priority == 'high']),
            'reviews_this_quarter': len([r for r in review_data if r.review_date >= datetime.now() - timedelta(days=90)])
        }
        
        return summary

Workload_Review_Process:
  process_id: "WRP-2024-001"
  process_name: "Quarterly Workload Optimization Review"
  
  review_framework:
    review_types:
      comprehensive_annual:
        frequency: "annual"
        duration: "6 weeks"
        scope: "full_architecture_review"
        stakeholders: ["technical_teams", "business_owners", "finance", "security"]
        
      quarterly_optimization:
        frequency: "quarterly"
        duration: "3 weeks"
        scope: "cost_optimization_focus"
        stakeholders: ["technical_teams", "cost_optimization_team"]
        
      monthly_service_evaluation:
        frequency: "monthly"
        duration: "1 week"
        scope: "new_service_assessment"
        stakeholders: ["technical_leads", "architects"]
        
  evaluation_criteria:
    cost_impact:
      weight: 0.30
      excellent: ">30% cost reduction"
      good: "15-30% cost reduction"
      neutral: "±15% cost impact"
      poor: ">15% cost increase"
      
    technical_fit:
      weight: 0.25
      excellent: "Perfect fit with enhanced capabilities"
      good: "Good fit with minor gaps"
      neutral: "Adequate fit with workarounds"
      poor: "Poor fit requiring significant changes"
      
    implementation_effort:
      weight: 0.20
      excellent: "Minimal effort, drop-in replacement"
      good: "Moderate effort, straightforward migration"
      neutral: "Significant effort, complex migration"
      poor: "Extensive effort, major architectural changes"
      
  review_schedule:
    q1_2024:
      - workload: "e-commerce-platform"
        type: "comprehensive_annual"
        scheduled_date: "2024-01-15"
        
      - workload: "data-analytics-pipeline"
        type: "quarterly_optimization"
        scheduled_date: "2024-01-22"
        
    q2_2024:
      - workload: "mobile-api-backend"
        type: "quarterly_optimization"
        scheduled_date: "2024-04-15"
        
  automation_components:
    service_monitoring:
      aws_whats_new_monitor: true
      pricing_change_alerts: true
      feature_update_tracking: true
      
    data_collection:
      workload_inventory_automation: true
      cost_data_collection: true
      performance_metrics_gathering: true
      
    evaluation_automation:
      service_relevance_scoring: true
      cost_impact_calculation: true
      risk_assessment_automation: true
      
  success_metrics:
    review_completion_rate: ">95%"
    optimization_opportunities_identified: ">10 per quarter"
    estimated_savings_identified: ">$50,000 per quarter"
    implementation_success_rate: ">80%"
    
  governance:
    review_approval_authority:
      - role: "Technical Lead"
        scope: "technical_recommendations"
        
      - role: "Finance Manager"
        scope: "cost_impact_decisions"
        
      - role: "CTO"
        scope: "strategic_architecture_changes"
        
    documentation_requirements:
      - "Review findings and analysis"
      - "Cost-benefit calculations"
      - "Risk assessment and mitigation plans"
      - "Implementation roadmap and timeline"
      - "Success metrics and monitoring plan"